Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3121–3130 of 10307 papers

Title	Date	Tasks	Status
Bootstrap an end-to-end ASR system by multilingual training, transfer learning, text-to-text mapping and synthetic audio	Nov 25, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation	May 4, 2023	Imitation LearningTransfer Learning	—Unverified
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity	Oct 22, 2021	Active LearningDomain Adaptation	—Unverified
Domain Adaptation and Transfer Learning in StochasticNets	Dec 18, 2015	BIG-bench Machine LearningDomain Adaptation	—Unverified
Domain Adaptation-based Edge Computing for Cross-Conditions Fault Diagnosis	Nov 15, 2024	DiagnosticDomain Adaptation	—Unverified
Domain Adaptation Broad Learning System Based on Locally Linear Embedding	Jun 28, 2021	ClassificationComputational Efficiency	—Unverified
Deep Learning-Based Communication Over the Air	Jul 11, 2017	Deep LearningTransfer Learning	—Unverified
Domain Adaptation by Topology Regularization	Jan 28, 2021	Domain AdaptationTopological Data Analysis	—Unverified
Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts	Sep 22, 2023	ArticlesBenchmarking	—Unverified
Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning	Dec 15, 2020	Deep LearningGeneral Classification	—Unverified

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All datasets Office-Home 100 sleep nights of 8 caregivers BanglaLekha Isolated Dataset COCO70 KITTI Object Tracking Evaluation 2012 Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	APCLIP	Accuracy	84.2	—	Unverified
2	DFA-ENT	Accuracy	69.2	—	Unverified
3	DFA-SAFN	Accuracy	69.1	—	Unverified
4	EasyTL	Accuracy	63.3	—	Unverified
5	MEDA	Accuracy	60.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	10-20% Mask PSNR	3.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Chatterjee, Dutta et al.[1]	Accuracy	96.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Co-Tuning	Accuracy	85.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Physical Access	EER	5.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	riadd.aucmedi	AUROC	0.95	—	Unverified